{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data=pd.read_excel('./data/供应链商品销售数据.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=data.loc[data['销售点类型']=='社区店']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>商品代号</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>销售点类型</th>\n",
       "      <th>销售额（万元）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Safety 8</td>\n",
       "      <td>August</td>\n",
       "      <td>社区店</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Safety 2</td>\n",
       "      <td>February</td>\n",
       "      <td>社区店</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Safety 8</td>\n",
       "      <td>November</td>\n",
       "      <td>社区店</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tape 10</td>\n",
       "      <td>October</td>\n",
       "      <td>社区店</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Safety 8</td>\n",
       "      <td>January</td>\n",
       "      <td>社区店</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>Safety 2</td>\n",
       "      <td>July</td>\n",
       "      <td>社区店</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>Safety 8</td>\n",
       "      <td>September</td>\n",
       "      <td>社区店</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>Tape 10</td>\n",
       "      <td>August</td>\n",
       "      <td>社区店</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>Tape 10</td>\n",
       "      <td>November</td>\n",
       "      <td>社区店</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>Tape 6</td>\n",
       "      <td>October</td>\n",
       "      <td>社区店</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>646 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          商品代号       销售月份 销售点类型  销售额（万元）\n",
       "1     Safety 8     August   社区店     10.0\n",
       "2     Safety 2   February   社区店     10.0\n",
       "3     Safety 8   November   社区店     10.0\n",
       "4      Tape 10    October   社区店      2.5\n",
       "5     Safety 8    January   社区店     10.0\n",
       "...        ...        ...   ...      ...\n",
       "1315  Safety 2       July   社区店     10.0\n",
       "1316  Safety 8  September   社区店     10.0\n",
       "1318   Tape 10     August   社区店      2.5\n",
       "1321   Tape 10   November   社区店      2.5\n",
       "1324    Tape 6    October   社区店      2.5\n",
       "\n",
       "[646 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data['销售点类型']=='社区店']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "data2=data1[['销售点类型','销售额（万元）']].groupby(by='销售点类型')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "number=data2.agg(np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售额（万元）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>销售点类型</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>社区店</th>\n",
       "      <td>4606.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       销售额（万元）\n",
       "销售点类型         \n",
       "社区店     4606.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.agg(np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data3=data.loc[data['销售点类型']=='CBD店']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>商品代号</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>销售点类型</th>\n",
       "      <th>销售额（万元）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tape 10</td>\n",
       "      <td>April</td>\n",
       "      <td>CBD店</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Safety 8</td>\n",
       "      <td>December</td>\n",
       "      <td>CBD店</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Safety 1</td>\n",
       "      <td>September</td>\n",
       "      <td>CBD店</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Safety 8</td>\n",
       "      <td>August</td>\n",
       "      <td>CBD店</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Safety 8</td>\n",
       "      <td>October</td>\n",
       "      <td>CBD店</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>Safety 1</td>\n",
       "      <td>September</td>\n",
       "      <td>CBD店</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>Safety 8</td>\n",
       "      <td>May</td>\n",
       "      <td>CBD店</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1322</th>\n",
       "      <td>Safety 1</td>\n",
       "      <td>October</td>\n",
       "      <td>CBD店</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1323</th>\n",
       "      <td>Safety 8</td>\n",
       "      <td>October</td>\n",
       "      <td>CBD店</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>Safety 8</td>\n",
       "      <td>December</td>\n",
       "      <td>CBD店</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>680 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          商品代号       销售月份 销售点类型  销售额（万元）\n",
       "0      Tape 10      April  CBD店      2.5\n",
       "6     Safety 8   December  CBD店     10.0\n",
       "7     Safety 1  September  CBD店     12.0\n",
       "11    Safety 8     August  CBD店     10.0\n",
       "12    Safety 8    October  CBD店     10.0\n",
       "...        ...        ...   ...      ...\n",
       "1319  Safety 1  September  CBD店     12.0\n",
       "1320  Safety 8        May  CBD店     10.0\n",
       "1322  Safety 1    October  CBD店     12.0\n",
       "1323  Safety 8    October  CBD店     10.0\n",
       "1325  Safety 8   December  CBD店     10.0\n",
       "\n",
       "[680 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data['销售点类型']=='CBD店']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "data4=data3[['销售点类型','销售额（万元）']].groupby(by='销售点类型')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售额（万元）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>销售点类型</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CBD店</th>\n",
       "      <td>4985.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       销售额（万元）\n",
       "销售点类型         \n",
       "CBD店    4985.5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.agg(np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\pyecharts\\charts\\chart.py:14: PendingDeprecationWarning: pyecharts 所有图表类型将在 v1.9.0 版本开始强制使用 ChartItem 进行数据项配置 :)\n",
      "  super().__init__(init_opts=init_opts)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"4b86b4be5b264d358e42992b82e8091f\" style=\"width:720px; height:320px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_4b86b4be5b264d358e42992b82e8091f = echarts.init(\n",
       "                    document.getElementById('4b86b4be5b264d358e42992b82e8091f'), 'white', {renderer: 'canvas'});\n",
       "                var option_4b86b4be5b264d358e42992b82e8091f = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                4985.5,\n",
       "                4606.5\n",
       "            ],\n",
       "            \"showBackground\": false,\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"20%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"CBD\\u5e97\",\n",
       "                \"\\u793e\\u533a\\u5e97\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u603b\\u7684\\u9500\\u552e\\u989d\\u5bf9\\u6bd4\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_4b86b4be5b264d358e42992b82e8091f.setOption(option_4b86b4be5b264d358e42992b82e8091f);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x19f71878648>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.charts import Bar\n",
    "import pyecharts.options as opts\n",
    "\n",
    "num=['CBD店','社区店']\n",
    "num2=[4985.5,4606.5]\n",
    "(\n",
    "      Bar(init_opts=opts.InitOpts(width='720px',height='320px'))#画布大小\n",
    "      .add_xaxis(xaxis_data=num)\n",
    "      .add_yaxis(series_name='',y_axis=num2)\n",
    "      .set_global_opts(title_opts=opts.TitleOpts(title='总的销售额对比'))\n",
    ").render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type = pd.pivot_table(data[['商品代号', '销售点类型', '销售额（万元）']], \n",
    "               index=['销售点类型', '商品代号'],aggfunc=np.sum) # 创建透视表 （形成两店的商品代号与销售额关系表）\n",
    "\n",
    "type1 = type.loc['CBD店'] # 切分CBD店（CBD店商品代与销售额关系表）\n",
    "type2 = type.loc['社区店'] # 切分社区店（社区店商品代号与销售额关系表）\n",
    "xtext = type1.index # 商品代号序列\n",
    "# 折线图 ： plt.plot()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 横坐标销售点类型\n",
    "\n",
    "plt.figure(figsize=(30, 15), facecolor = 'lightgray') # 设置画布大小\n",
    "plt.style.use('ggplot') #设置背景风格\n",
    "plt.plot(range(len(type1+1)), type1['销售额（万元）']) # 绘制CBD店商品代号与销售额关系的折线图\n",
    "plt.plot(range(len(type2+1)), type2['销售额（万元）']) # 绘制社区店商品代号与销售额关系的折线图\n",
    "\n",
    "# 设置matplotlib正常显示中文和负号\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "\n",
    "# 对应位置 添加注释 （对齐方式垂直方向ha&水平方向va） \n",
    "for i,j in zip( range(len(type1)),type1['销售额（万元）'] ):\n",
    "    plt.text(i, j, '%.0f'%j, ha='center', va='bottom')\n",
    "for i,j in zip( range(len(type2)),type2['销售额（万元）'] ):\n",
    "    plt.text(i, j, '%.0f'%j, ha='center', va='bottom')\n",
    "\n",
    "plt.xlabel('商品代号')   # 行标签\n",
    "plt.ylabel('销售额（万元）')   # 列标签\n",
    "plt.legend(['CBD店', '社区店'])  # 自动匹配第一个第二个线的标签\n",
    "plt.xticks(range(0,30),xtext, rotation=30) # 设置x坐标轴标签 （rotation 旋转角度）\n",
    "plt.title('各类商品不同销售点销售差额对比图') # 标题\n",
    "plt.show()"
   ]
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